PROJECT SUMMARY Small cell lung cancer (SCLC) is lung cancer of neuroendocrine origin, characterized by aggressive growth and rapid spread. Initially responsive to treatment, SCLC nearly invariably relapses and leads to patient death within 2 years. Recent work has shown that SCLC is a highly heterogeneous disease comprising multiple cellular subtypes within a tumor that exhibit differential sensitivity to drug treatments. However, SCLC research to date has focused on two major areas, which have not yet been unified. Cell-molecular studies have revealed two broad phenotypic subtypes within a tumor population, neuroendocrine (NE) and non-neuroendocrine (non- NE). Receptor-ligand interactions such as those between Notch receptor and its ligand DLL4 have been found to play a role in inducing transformations between these different cell types, such as the transition from NE cells into a non-NE phenotype. Single-cell RNA sequencing approaches have produced transcriptional data showing regulatory pathways active in SCLC tumor cells, which drive cells towards broadly NE or non-NE cell processes. While both cell-signaling approaches and transcriptional approaches have provided valuable insights into SCLC phenotypic subtype differentiation, it is not clear how transcriptional regulation of subtypes is linked to cell population behaviors that enable tumor growth. A systems-level picture of SCLC tumor behavior is not achievable without understanding how intercellular dynamics and cell-cell signaling in the tumor directly affects transcriptional activation, and how transcriptional activation leading to subtype transitions affects these intercellular dynamics. I present an approach to study both of these mechanisms of phenotypic subtype differentiation in SCLC, using computational modeling of population dynamics to characterize cell-cell interactions and tumor makeup with regard to phenotypic subtypes, and Boolean logic modeling to characterize transcriptional signaling pathways intracellularly to provide detail about how different transcription factors can result in differentiation into each phenotypic subtype. I will also study both of these processes experimentally, investigating means by which to destabilize each subtype and lead to inhibition of tumor growth, while using results to further refine my mathematical models. Studying each area in the context of the other is expected to improve knowledge of tumor behavior. Enhanced characterization of tumor subtype sensitivities with regard to growth or phenotypic transition will lead to new therapeutic strategies for SCLC patients, which will be extremely significant in improving patient outcomes. This project will result in a systems- level understanding of the correspondence between transcriptional networks and tumor subtype composition in SCLC, bringing to light targeted treatment options to destabilize the system.